[Removal of artifacts from EEG signal]
Xiaoyan Du1, Yingjie Li, Yisheng Zhu
1Biomedical Engineering Institute of Communication and Information Engineering School, Shanghai University, Shanghai 200072, China.
Summary
This review covers methods for removing artifacts from electroencephalogram (EEG) recordings. It details techniques like PCA and ICA to improve brain signal clarity.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Context:
- Electroencephalogram (EEG) signals are crucial for understanding brain activity.
- EEG recordings are frequently contaminated by artifacts from eye movements, muscle activity, and cardiac signals.
- These artifacts significantly hinder accurate EEG interpretation and data analysis.
Purpose:
- To provide a comprehensive review of recent artifact rejection methods for EEG.
- To summarize the assumptions, advantages, and disadvantages of various artifact removal techniques.
- To aid researchers in selecting appropriate methods for cleaning EEG data.
Summary:
- This paper reviews common artifact rejection techniques used in electroencephalogram (EEG) signal processing.
- Methods discussed include regression-based approaches, artifact subtraction, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and wavelet transforms.
- Each method's underlying principles, strengths, and limitations are analyzed.
Impact:
- Improved accuracy and reliability of EEG data analysis.
- Facilitation of clearer insights into brain function and neurological conditions.
- Guidance for researchers in selecting optimal artifact removal strategies for their specific EEG studies.
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